Statistical active learning for efficient value function approximation in reinforcement learning (ニューロコンピューティング)
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概要
- 論文の詳細を見る
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares policy iteration (LSPI) framework allows us to employ statistical active learning methods for linear regression. Then we propose a design method of good sampling policies for efficient exploration, which is particularly useful when the sampling cost of immediate rewards is high. The proposed method combined with LSPI is called active policy iteration (API). Through simulations we demonstrate the usefulness of API.
- 2009-03-04
著者
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Sugiyama Masashi
Tokyo Inst. Of Technol. Tokyo Jpn
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Hachiya Hirotaka
Department Of Computer Science Tokyo Institute Of Technology
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SUGIYAMA Masashi
Department of Computer Science, Tokyo Institute of Technology
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Sugiyama Masashi
Tokyo Inst. Of Technol.
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Akiyama Takayuki
Department of Computer Science, Tokyo Institute of Technology
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Akiyama Takayuki
Department Of Computer Science Tokyo Institute Of Technology
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Sugiyama Masashi
Department Of Computer Science Tokyo Institute Of Technology
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Hachiya Hirotaka
Tokyo Inst. Of Technol.
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Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
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Sugiyama Masashi
Department of Applied Chemistry, Yamanashi University
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